It’s a funny thing, AI. It can identify objects in a fraction of a second, imitate the human voice and recommend new music, but most machine “intelligence” lacks the most basic understanding of everyday objects and actions — in other words, common sense. DARPA is teaming up with the Seattle-based Allen Institute for Artificial Intelligence to see about changing that. The Machine Common Sense program aims to both define the problem and engender progress on it, though no one is expecting this to be “solved” in a year or two. But if AI is to escape the prison of the hyper-specific niches where it works well, it’s going to need to grow a brain that does more than execute a classification task at great speed. “The absence of common sense prevents an intelligent system from understanding its world, communicating naturally with people, behaving reasonably in unforeseen situations, and learning from new experiences. This absence is perhaps the most significant barrier between the narrowly focused AI applications we have today and the more general AI applications we would like to create in the future,” explained DARPA’s Dave Gunning in a press release. Not only is common sense lacking in AIs, but it’s remarkably difficult to define and test, given how broad the concept is. Common sense could be anything from understanding that solid objects can’t intersect to the idea that the kitchen is where people generally go when they’re thirsty. As obvious as those things are to any human more than a few months old, they’re actually quite sophisticated constructs involving multiple concepts and intuitive connections. WTF is AI? It’s not just a set of facts (like that you must peel an orange before you eat it, or that a drawer can hold small items) but identifying connections between them based on what you’ve observed elsewhere. That’s why DARPA’s proposal involves building “computational models that learn from experience and mimic the core domains of cognition as defined by developmental psychology. This includes the domains of objects (intuitive physics), places (spatial navigation) and agents (intentional actors).” But how do you test these things? Fortunately, great minds have been at work on this problem for decades, and one research group has proposed an initial method for testing common sense that should work as a stepping stone to more sophisticated ones.

Ubergizmo Razer Phone 2 Leaked Ahead Of Official Announcement Ubergizmo The Razer Phone 2 is expected to be officially announced in the next couple of hours. However ahead of the official announcement, it seems that someone at Amazon Italy might have allowed the listing of the phone to go live earlier than expected thus ... Razer Phone 2 - Flagship // Gaming Razer all 86 news articles »

Manfrotto has redesigned its 492 and 492LCD centre ball heads and is expanding its range with the new 490 model, the simplest head in the range. The MH492-BH SRP inc VAT is £54.95, the MH492LCD-BH is £64.95, and the MH490-BH SRP inc VAT is £44.95.

Tesla is planning to bring games to its electric cars' giant touchscreens, so why doesn't it have video playback while the car isn't moving -- something drivers have wanted for years? Don't worry, you'll get your wish soon. Elon Musk has responded to...

In the future everyone will be naked for fifteen minutes. It’s with this novel thought in mind that I connect with a model named Nazanin who will walk me through the new world of bi-directional teledildonic cam life. I was there to test a new device from Kiiroo called the Kiiroo Launch. This novel sex jar connects with a Flashlight – essentially a masturbator – and can send and receive signals from a remote dildo. When I first explored the Kiiroo system three years ago and found it fascinating although, arguably, it was like having sex with a 3D printer. And so I was ready to work with Nazanin. This is going to be NSFW by the way. Bi-directional, you say? In the world of cam-based teledildonics the models usually wear some sort of vibrator connected to a tipping system. When the viewer tips them the model’s vibrator vibrates, adding a frisson of interactivity to what is usually a one-way street. This became the norm for most cam sites and the Lush from Lovense is a popular choice in the current cam world.

Ubergizmo iPhone 7, 7 Plus Owners Are Encountering 'Loop Disease' Ubergizmo As phones get older, they start to fail in a variety of ways. This is a natural process due to wear and tear. However when phones start to fail in a similar way, that's when it starts to get a bit suspicious, as is the case with the iPhone 7 and 7 Plus ... Some iPhone 7 And 7 Plus Owners Experiencing 'Loop Disease' Geeky Gadgets all 3 news articles »

While some of the largest technology companies in the world are racing to figure out the next generation of machine learning-focused chips that will support devices — whether that’s data centers or edge devices — there’s a whole class of startups that are racing to get there first. That includes Cerebras Systems , one of the startups that has raised a significant amount of capital, which is looking to continue targeting next-generation machine learning operations with the hiring of Dhiraj Mallick as its Vice President of Engineering and Business Development. Prior to joining Cerebras, Mallick served as the VP of architecture and CTO of Intel’s data center group. That group generated more than $5.5 billion in the second quarter this year, up from nearly $4.4 billion in the second quarter of 2017, and has generated more than $10 billion in revenue in the first half of this year. Prior to Intel, Mallick spent time at AMD and SeaMicro. That latter part is going to be a big part of the puzzle, as Google looks to lock in customers in its cloud platform with tools like the Tensor Processing Unit, the third generation of which was announced at Google I/O earlier this year . Data centers are able to handle some of the heavy lifting when it comes to training the models that handle machine learning processes like image recognition as they don’t necessarily have to worry about space (or partly heat, in the case of the TPU running with liquid cooling) constraints. Google is betting on that with the TPU, optimizing its hardware for its TensorFlow machine learning framework and trying to build a whole developer ecosystem that it can lock into its hardware with that and its new edge-focused TPU for inference . Cerebras Systems is one of a class of startups that want to figure out what the next generation of machine hardware looks like, and most of them have raised tens of millions of dollars. It’s one of the startups that has been working on its technology for a considerable amount of time.

The artificial intelligence revolution is underway in the world of technology, but as it turns out, some of the most faithful foot soldiers are still humans. A startup called Scale , which works with a team of contractors who examine and categorise visual data to train AI systems in a two-sided marketplace model, announced that it has raised an additional $18 million in a Series B round. The aim will be to expand Scale’s business to become — in the words of CEO Alexandr Wang, the 21-year-old MIT grad who co-founded Scale with Lucy Guo — “the AWS of AI, with multiple services that help companies build AI algorithms.” “Our mission is to accelerate the development of AI apps,” Wang said. “The first product is visual data labelling, but in the future we have a broad vision of what we hope to provide.” Wang declined to comment on the startup’s valuation in an interview. But according to Pitchbook , which notes that this round actually closed in May of this year, the post-money valuation of Scale is now $93.50 million ($75 million pre-money). The money comes on the back of an eventful two years since the company first launched, with revenues growing 15-fold in the last year, and “multiple millions of dollars in revenue” from individual customers. (It doesn’t disclose specific numbers, however.) Today, Scale’s base of contractors numbers around 10,000, and it works with a plethora of businesses that are developing autonomous vehicle systems such as General Motors’ Cruise, Lyft Zoox, Nuro, Voyage, nuTonomy and Embark. These companies send Scale’s contractors raw, unlabelled data sets by way of Scale’s API, which provides services like Semantic Segmentation, Image Annotation, and Sensor Fusion, in conjunction with its clients LIDAR and RADAR data sets. In total, it says it’s annotated 200,000 “miles of data” collected by self-driving cars. AV companies are not its only customers, though. Scale also works with several non-automotive companies like Airbnb and Pinterest, to help build their AI-based visual search and recommendation systems. Airbnb, for example, is looking for more ways of being able to ascertain what kinds of homes repeat customers like and don’t like, and also to start to provide other ways of discovering places to stay that are based not just on location and number of bedrooms (which becomes more important especially in cities where you may have too many choices and want a selection more focused on what you are more likely to rent)

Ubergizmo Apple Might Not Bundle Lightning-To-Headphone Jack Converter With 2018 iPhones Ubergizmo When Apple launched the iPhone 7, they made a rather bold choice by eliminating the headphone jack from the phone. The debate about whether or not the 3.5mm jack is still necessary in today's tech landscape is still ongoing, but safe to say that this ... Rumour Claims Apple Won't Put Headphone Adaptors in This Year's iPhones Gizmodo UK all 28 news articles »

Ubergizmo Apple Working With Chinese Carriers To Reduce Spam Ubergizmo Recently you might have heard reports of how Chinese state-run media have leveled accusations and criticisms against Apple for not doing enough to fight against spam, where it seems that iPhone users in the country were receiving messages for illegal ... and more »

Teslas are nothing if not giant batteries on wheels, so it would only make sense if you could use the battery for something other than getting from A to B, wouldn't it? You will soon. Elon Musk has teased the future addition of a "party & campe...

Every artificial intelligence startup or corporate R&D lab has to reinvent the wheel when it comes to how humans annotate training data to teach algorithms what to look for. Whether it’s doctors assessing the size of cancer from a scan or drivers circling street signs in self-driving car footage, all this labeling has to happen somewhere. Often that means wasting six months and as much as a million dollars just developing a training data system. With nearly every type of business racing to adopt AI, that spend in cash and time adds up. Labelbox builds artificial intelligence training data labeling software so nobody else has to. What Salesforce is to a sales team, Labelbox is to an AI engineering team. The software-as-a-service acts as the interface for human experts or crowdsourced labor to instruct computers how to spot relevant signals in data by themselves and continuously improve their algorithms’ accuracy. Today, Labelbox is emerging from six months in stealth with a $3.9 million seed round led by Kleiner Perkins and joined by First Round and Google’s Gradient Ventures. “There haven’t been seamless tools to allow AI teams to transfer institutional knowledge from their brains to software,” says co-founder Manu Sharma. “Now we have over 5,000 customers, and many big companies have replaced their own internal tools with Labelbox.” Kleiner’s Ilya Fushman explains that “If you have these tools, you can ramp up to the AI curve much faster, allowing companies to realize the dream of AI.” Inventing the best wheel Sharma knew how annoying it was to try to forge training data systems from scratch because he’d seen it done before at Planet Labs, a satellite imaging startup. “One of the things that I observed was that Planet Labs has a superb AI team, but that team had been for over six months building labeling and training tools. Is this really how teams around the world are approaching building AI?,” he wondered

The Indian Express Alleged 6.5-inch & 6.1-inch iPhone (2018) Dummy Units Surface Ubergizmo According to the rumors, Apple could be launching three iPhone models this year. One will retain the size and design of the current iPhone X, while the other two will feature a 6.1-inch LCD iPhone model, and a 6.5-inch OLED iPhone model. Now thanks to ... New iPad Pro said to drop 3.5mm jack, move Smart Connector to bottom to accommodate vertical-only Face ID 9to5Mac all 136 news articles »

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